Abstract 2891: Landscape of infiltrating immune repertoire in pediatric solid tumors
Bibliographic record
Abstract
Abstract Introduction: Immune repertoire is a highly diverse pool of B and T cell receptors (B/TCR) that determine boundaries of immune surveillance. Pre-existing immune clones within Tumor MicroEnvironment (TME) suggest existence of a functional antigen presenting machinery and recognizing T cells, which fail to eliminate tumor, likely due to immunosuppressive environment. Shifts in composition of immune repertoire upon immune checkpoint blockade have been reported in adult cancers and shown to be associated with clinical response. As immunotherapy is being introduced to pediatric oncology, there is a need to better understand immunogenomic aspects of TME in childhood cancers. Objective: We hypothesize that characteristics of TCRs in conjunction with gene expression profile of immune cells are key determinants of immune response in pediatric patients. Methods: We compiled a pan-pediatric cohort and associated RNAseq datasets through multiple research initiatives. Participating programs include NCI TARGET (n ~ 270), International Cancer Genome Consortium (ICGC, n ~ 250) and Children’s Brain Tumor Tissue Consortium (CBTTC, n ~ 790). As comparator, we analyzed 7 adult cancer types as well as data from constitutive mismatch repair deficiency (CMMRD) consortium. We devised a simple and reliable index to estimate immune diversity. We used CIBERSORT tool to infer immune fraction of TME and investigated immune-related gene expression. In partnership with Gabriela Miller’s Kids First Data Resource Centre, analyses were performed on CAVATICA computational platform. Results: While median number of RNAseq reads were comparable across TARGET, CBTTC and TCGA datasets (~ 67-90 million reads), ICGC dataset contained a median of ~ 208 million reads. However, number of reads mapped to immune loci were comparable across all datasets and appeared to be confounded by infiltration extent rather than technical discordance. Our preliminary results indicate Neuroblastoma harbored the highest median of inferred diversity across pediatric cancers (TRβ=59.38). Inferred immune content indicated a range of infiltration from 0.59 in Teratoma/Germinoma to 0.15 in Medulloblastoma. Immune checkpoint gene expression across pediatric cancers show overall downward trend compared to adult counterparts. Conclusions: Our comprehensive study will serve as a common ground for researchers to delineate immune component of pediatric tumor microenvironment. Covered in this study are common solid tumors as well as rare malignancies, characterization of which will aid rational design of immunotherapy-related clinical trials of pediatric cancers. Note: This abstract was not presented at the meeting. Citation Format: Arash Nabbi, Natalie Jäger, Sumedha Sudhaman, Pengbo Sun, S. Y. Cindy Yang, Kelsey Zhu, Marcel Kool, Komal Rathi, Karthik Kalletla, Pichai Raman, Yuankun Zhu, Joseph N. Paulson, David T. Jones, Uri Tabori, Adam C. Resnick, Stefan M. Pfister, Trevor J. Pugh. Landscape of infiltrating immune repertoire in pediatric solid tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2891.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".